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ISSN: 2079-5882
Jerusalem Papers in Regulation & Governance
Working Paper No. 13 June 2010
POLITICAL DETERMINANTS OF LIBERALIZATIONS;
NEW EVIDENCE FROM OECD
NETWORK INDUSTRIES
Filippo Belloc
Department of Economics,
Faculty of Economics “R. M. Goodwin”
University of Siena, Italy
P.zza S. Francesco 7 53100
Email: [email protected]
Antonio Nicita
Department of Economics,
Faculty of Economics “R. M. Goodwin”
University of Siena, Italy
P.zza S. Francesco 7 53100
Homepage: http://www.antonionicita.it
Email: [email protected]
Jerusalem Forum
on Regulation & Governance
The Hebrew University
Mount Scopus
Jerusalem, 91905, Israel
הפורום הירושלמילרגולציה וממשליות
האוניברסיטה העברית הר הצופים
91905, ירושלים
Email :[email protected]
http://regulation.huji.ac.il
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Political Determinants of Liberalizations;
New Evidence from OECD Network Industries
Filippo Belloc and Antonio Nicita
Abstract: We investigate the political determinants of liberalization in OECD network
industries. Performing a panel estimation over thirty years, through the largest and most
updated sample available, we find that right-wing governments liberalize less than left-
wing ones. This result, which contrasts conventional wisdom, is confirmed when
controlling for the existing regulatory conditions that executives find when elected.
Furthermore, governments‟ heterogeneity, proportional electoral rules, and European
Union membership all show positive and statistically significant effects on liberalization
in network industries. We conclude that traditional ideological cleavage does not
provide a political-economic rationale for the observed liberalization paths.
Acknowledgements:
We would like to thank for previous discussions on the issue or comments Carlo
Cambini, Xavier Fernandez I Marin, Philippe Martin, Fabio Padovano and Stefan
Voigt,. Usual disclaimers apply. This research is part of the REFGOV project.
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Political Determinants of Liberalizations;
New Evidence from OECD Network Industries
1. Introduction
One of the distinguishing features of the last three decades has been the wave of market-
oriented policies experienced worldwide (Conway and Nicoletti, 2006; Armstrong and
Sappington, 2006; Guriev and Megginson, 2007; Pitlik, 2007).
In particular, in network industries, which cover crucial sectors for national economies
such as passenger air transport, telecommunications, electricity, gas, post, rail and road,
privatization and liberalization are among the market-oriented policies which registered
the largest convergence across OECD countries.
Beside the analysis of economic determinants (Vickers and Yarrow, 1991; Levy and
Spiller, 1996; Newbery, 1997; 2002; Armstrong and Sappington, 2006), a large group
of scholars have investigated the role of institutional and political determinants of
market-oriented policy in network industries, following the „political economics‟
approach (Alesina, 1988; Alesina and Rosenthal, 1995; Perrotti, 1995; Garrett, 1998;
Persson and Tabellini, 2000; Persson, 2002; Besley and Case, 2003; Besley, 2007;
Besley, Persson and Sturm, 2010).
While the political economy of privatization in network industries has been largely
investigated and measured, little of this literature, with some relevant exceptions,
addresses the role of political parties and institutions as determinants of liberalization
policy.
In this paper we attempt to fill this gap, analyzing liberalization policies over the last
three decades in seven OECD network industries, whose relevance is crucial both in
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terms of their impact on per capita consumption and as suppliers of intermediate inputs
(Conway and Nicoletti, 2006). Liberalization here refers mainly to policy aimed at
reducing economic, institutional and legal barriers to entry in sectors previously
dominated by legal state-owned monopolies and in which access to essential facility
networks is crucial to develop downstream competition.
Our analysis is based on the largest updated data set available, provided by the latest
releases of ETCR economic indicators for liberalization (OECD, 2009) and by political
indicators of the World Bank‟s Database of Political Institutions (World Bank, 2009).
This allows us to consider a larger group of countries and a longer period of time with
respect to previous empirical studies. In particular, we perform a panel analysis on a
sample of 30 OECD countries over the 1975-2006 period, and circumvent omitted
variable bias and endogeneity problems by estimating a time/country fixed effects lag-
model. Our results, firstly, reveal – contrary to previous analyses (Pitlik, 2007, Potrafke,
2009) - that left-wing governments have been more active in promoting liberalization
policies than right-wing ones. Secondly, they suggest that the traditional claim of right-
wing governments to be the biggest promoters of market-oriented policies may need to
be reconsidered when the deregulation process is analyzed in its entire accomplishment
from the Seventies to date.
Moreover, our analysis reveals that government heterogeneity and proportional electoral
systems show a positive and statistically significant effect on the decision to liberalize.
Also international openness of internal markets and European Union membership, show
a positive and statistically significant effect on the intensity of liberalization policies. In
addition, adoption of the euro currency shows a statistical significant effect on
liberalization in network industries, as pointed out by Høj, Galasso, Nicoletti and Dang
(2006). Finally, a strong path-dependency is found for OECD liberalization patterns, as
past deregulation initiatives not only show a remarkable „ratchet effect‟ but also seem to
generate a positive attitude in governments towards launching new liberalization
programs.
We conclude that traditional ideological cleavage does not provide, as in the past, a
political-economic rationale behind liberalization paths in network industries.
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Two caveats arise: we do not explore whether political parties properly reflect, in their
liberalization choices, the interests of their constituents; and we do not measure
economic liberalization outcomes such as prices, market structure, investments and so
on. The paper proceeds as follows. In Section 2 we briefly review the existing empirical
literature. In section 3 we outline some styized facts over political determinants of
liberalization in selected OECD countries. In Section 4 we describe our data and
empirical strategy, while in Section 5 we summarize our main results. Section 6
discusses our findings and Section 7 concludes.
2. Related Literature
We devote here our attention to the literature on the political and institutional
determinants of privatization and liberalization policies in network industries.
Many scholars have attempted to analyze and measure the political and institutional
determinants of privatization in network industries, interpreting the degree of public
ownership as the most significant, if not exhaustive, proxy for the adoption of market-
oriented policies in developed economies (Perotti, 1995; Boix, 1997; Meggison and
Netter, 2001; Li and Xu, 2002; Biais and Perotti, 2002; Schneider, Fink, and
Tenbucken, 2005; Dinc and Gupta, 2007; Bortolotti and Pinotti, 2008; Schneider and
Häge, 2008; Biørnskov and Potrafke, 2009; Arin and Ulubasoglu, 2009) .
The regularity observed by these empirical investigations shows that „politics matter‟
for the adoption of privatization policy and that the decision to privatize is significantly
influenced by majoritarian and right-wing governments, while proportional electoral
rules and left-wing governments seem traditionally to have hindered it.
While the political economy of privatization choices has been largely investigated, the
analysis of institutional and political determinants of liberalization policy is still in its
infancy. In the light of the evidence on the determinants of privatization, some scholars
have concluded that additionally other market-oriented policies in network industries,
such as liberalization, are mainly driven by right-wing parties in office. Duso (2002),
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studying regulatory intervention and entry liberalization within the mobile
telecommunications industry in OECD countries during the 1990s, shows how countries
with majoritarian elections liberalize more, with left-wing governments liberalizing less
than right-wing governments. Pitlik (2007) finds, for 22 OECD countries, that a left-
wing orientation of government and a high degree of legislative fragmentation are
negatively related to deregulation of markets. Duso and Seldeslachts (2009) investigate
liberalization in mobile telecommunications in 24 OECD countries, showing how a
majoritarian political system induces a faster liberalization, with right-wing parties
pushing more for market-oriented reforms. Finally, Potrafke (2009) analyzes the impact
of government ideology on the liberalization of network industries in 21 OECD
countries, showing how market-oriented and right-wing governments have been more
active in deregulating product markets, while European Union membership does not
turn out to be statistically significant.
The limited empirical literature so far available confirms, for liberalization policy in
network industries, the same conclusions as those reached in the literature on
privatization: a strong role for right-wing governments and majoritarian systems.
Besides, the impact of international policy diffusion and supranational determinants,
such as European Union membership, in inducing the adoption of market-oriented
policies seems more controversial, as Pitlik (2007) and Potrafke (2009) respectively
find the effects to be weak or absent, while Høj, Galasso, Nicoletti, and Dang (2006)
measure a significant impact of adoption of the euro for liberalization policies.
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3. Some Puzzling Stylized Facts
Some of the above preliminary conclusions reached in the literature, on the prominent
role of right wing party in promoting liberalization, turn out to be puzzling on empirical
grounds. Indeed, they seem apparently inconsistent with some relevant experiences of
liberalization processes observed in many developed economies. Several liberalization
reforms in network industries have been led by left-wing and centrist and/or
independent governments, proportional electoral rules and heterogeneous and „weak‟
governmental coalitions (Figure 1).
Figure 1: Aggregate liberalization race in seven network industries for 30 OECD
countries and right-wing/left-wing governments over the last decade.
(OECD, 2009; World Bank, 2008)
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
<2000 2000-2001 2002-2003 2004-2005 2006-2007
Right
Left
Note: liberalization is measured by subtracting the OECD‟s (2009) indicator of entry barriers
from its maximum value (the index ranges from 0 to 6): the liberalization initiatives‟ intensity
(Y axis) is then calculated as two-year variations of the liberalization index. On the right side of
the graph the average intensity before 2000 is displayed, on the left side two-year variations
after 2000 are shown.
Figure 1 above reports the aggregate liberalization race in seven network industries for
30 OECD countries. On the right side of the graph the average liberalization intensity
before 2000 is displayed, on the left side two-year variations after 2000 are shown.
Figure 1 makes it clear first of all that, within a sample of 30 OECD countries, before
2000 left-wing governments implemented some liberalization (measured by two-year
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variations in the inverse of ECTR index (OECD, 2009)) in the same vein as right-wing
governments, which however seem to have undertaken a higher level of liberalization
from an inferential point of view, as outlined by Pitlik (2007) and Potrafke (2009), who
study a smaller sample of OECD countries through 2002-03.
The new evidence clearly shows that, from the late Nineties onward, left-oriented
governments undoubtedly have been more active than right-oriented ones in
liberalization policies, while both right-wing and left-wing executives appear to have
reduced liberalization activity after 2006, having approached in many sectors the floor
of entry barrier reductions in the OECD indicator.
Some stylized facts, picked from the communications and electricity sectors, confirm
this pattern.
Some evidence of Left-Wing Liberalizations in the Communications Sector
With regard to the Danish communications sector, in 1999 the Social Democrats
approved an agreement enabling the establishment of alternative infrastructures in the
access network through the public tendering of frequency resources, in order to enhance
competition in the market. At the end of the same year, the Danish parliament, again
under the leadership of the Social Democrats, approved Act No. 1996 (amending the
Act on Radio-communications and Assignment of Radio Frequencies and the Act on
Public Mobile Communications) that substantially increased competition in broadband
services and in the mobile market. Similarly, in Portugal a government led by the
Socialist Party fully liberalized the telecommunications services between 1998 and
2000; in 2000, in particular, Portugal Telecom lost its exclusive rights as a telecoms
service provider. More generally, in the telecommunications sector, left-wing
governments have led substantive liberalization processes in several countries. In
France, the Socialists approved an unbundling decree in 2000 that mandated France
Telecom to provide both raw copper unbundling and shared access to its loops. In
Germany, the left-wing SPD approved in 2003 a new Telecommunications Act reducing
entry barriers. In Greece, Law No. 2246 of 1994 (introduced by the Pan-Hellenic
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Socialists) liberalized all telecommunications services and the mobile market. In
Hungary, the Hungarian Socialist Party enacted a number of regulatory initiatives
concerning licensing in telecommunications services between 1996 and 1998. In Italy,
the liberalization of satellite services and the voice telephony market started with Law
No. 249/97 by the Center-Left Ulivo alliance. In the Netherlands, the Labor Party
implemented the liberalization of telecommunications infrastructure and of all
telecommunications services between 1996 and 1997. In Poland, several ordinances
between 1996 and 2001 were implemented by SRP, and then by SLD, concerning
various aspects of the telecommunications market. In Spain, in 1995 the PSOE
approved the Satellite Telecommunications Act and the Cable Telecommunications Act
that authorized the concession for cable services through a call for tenders. In Turkey,
the parliament, led by the Democratic Left Party, approved the end of the monopoly of
Turk Telekom in 2000. In Canada, a number of liberalization initiatives for the
telecommunications market were implemented by the Liberal Party, starting from 1994.
Some evidence of Left-Wing Liberalizations in the Electricity Sector
Also in the electricity sector, left-wing governments have implemented pro-competitive
policies in the past. We recall here some concrete initiatives in brief. In Australia, the
left-wing government of Victoria passed an Electricity Industry Act in 1993 which
created a wholesale market. In Canada, the Electric Utilities Act was approved in
Alberta in 2001, and the Energy Competition Act passed in Ontario in 1998, both
through the promotion of the Liberal Party, completely liberalizing electricity supply. In
the Czech Republic, the Energy Act of 2000 was approved by a parliament dominated
by the Social Democratic Party – CSSD. In Denmark, the Amendment to the Danish
Electricity Supply Act issued in 1996 was approved by the Social Democrat-led
parliament, so permitting private companies and distribution companies of sufficient
size to buy power from third parties. In France, in 2000, Law No. 2000-108 concerning
the access of new entrants to both distribution and transmission networks was enacted
by the National Assembly under the Socialists. In Greece, the Electricity Law of 1999,
complying with Directive 96/92/EC and applying free market rules to electricity
generation and supply, was introduced by the Pan-Hellenic Socialists. In Italy, in 1999,
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with the Bersani Decree (Decree 79/99), the liberalization of the electricity sector and
the establishment of a sectoral Regulatory Authority were introduced by the Center-Left
coalition. In Japan, the first left-wing government since 1975 (led by the Social
Democratic Party – SDPJ) approved the Amendment to the Electricity Utility Law in
1995, introducing a system of competitive tendering in the wholesale electricity market.
In the Netherlands, with the Electricity Act of 1998 introduced by the Labor Party,
decentralized energy generation was favored. In Poland, the Energy Act was issued by
the SRP in 1997, allowing large electricity users to negotiate directly with generators of
power. In Spain, the Electricity Act was promoted by the Socialist Party – PSOE - in
1994, creating the Independent System and setting up competition for the access to
electricity networks. In Sweden, the Law for the Supply of Electricity 10/95 in 1995
was introduced by the Social Democrats, making it possible to generate and trade
electricity in a competitive environment. In Turkey, the Electricity Market Law of 2001,
establishing a new entity to oversee all energy market activities, was promoted by the
Democratic Left Party – DSP. Finally, in the USA, the Public Utility Regulatory
Policies Act – PURPA –, aimed at encouraging decentralized energy production, was
promoted by the Democrats in 1978 (approved along with the Airline Deregulation
Act).
General evidence of Left-Wing Liberalization Race in Network Industries
Besides the mentioned instances of launching liberalization in the communications and
electricity sectors, left-wing governments have been active – to differing extents - in
other industries, such as air transport, gas, post, rail and road.
As reported in Figure 2, with the exception of the road sector (where right-wing
governments seem to be far more active than left-wing ones) and of the post and gas
sector (where parties of either political leaning show a similar intensity in
liberalization), left-wing governments perform better than right-wing ones as to
liberalization intensity. We therefore claim that empirical analyses that refer to a limited
number of countries and that do not consider the deregulation process in its entirety
(from the Seventies to date) may lead only to partial conclusions. Indeed, especially in
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the last decade, many left-wing governments seem to have pushed convincingly towards
liberalization.
Figure 2: Liberalization race in seven network industries for 30 OECD countries and right-wing/left-wing governments over the 1975-2006 period.
(OECD, 2009; World Bank, 2008)
0
0.05
0.1
0.15
0.2
0.25
Electricity Gas Post Rail Road Telecom Air
Left
Right
Note: liberalization is measured by subtracting the OECD‟s (2009) indicator of entry barriers
from its maximum value (the index ranges from 0 to 6); liberalization initiatives‟ intensity (Y
axis) is then calculated as two-year variations of the liberalization index and expressed as an
average over the 1975-2006 period.
Finally, Figure 3 reports all the liberalization patterns (as an average for seven network
industries) observed in 15 of the 30 OECD countries analyzed in this paper in which
left-wing governments seemed to have pushed towards liberalization. The liberalization
race under left-wing governments is labeled by „L‟, whereas „R‟ stands for right-wing
governments, and NC for „other‟ parties according to the World Bank (2009)
classification.
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Figure 3: Performance of Left-Wing Governments on liberalization policy for seven
Network Industries
Source: Elaboration from OECD (2006, 2009) and World Bank (2009); L= left-wing;
R= right-wing; NC= non classified.
L R R R R R R R R L L L L L L L
L L L
L L
L
R
R
R R R R R RR R
23
45
6
Lib
era
lizatio
n
1970 1980 1990 2000 2010Years
Australia
L L L L L R L L L L
R R
R
R R R R R R L
L
L L L
L L L
L L L L L
12
34
5
Lib
era
lizatio
n
1970 1980 1990 2000 2010Years
Canada
L L L L L L L L L L L L L L L L NCNCR R
R
R RR L
L
L
L L
L
L
L
02
46
Lib
era
lizatio
n
1970 1980 1990 2000 2010Years
Czech Republic
R L L L L L L L R R R R R R
R R R R
R
L
LL L
L
L
LL
R RR
R R
02
46
Lib
era
lizatio
n
1970 1980 1990 2000 2010Years
Denmark
R R NCNCR R R L L L L
L R R
L L L L
L R R R R
LL
L L
L
R R
R R
01
23
45
Lib
era
lizatio
n
1970 1980 1990 2000 2010Years
France
NCNCNCNCNCNCR R R R R R L L L L R R
R L L L L L L L
L
L L L
R
R
01
23
4
Lib
era
lizatio
n
1970 1980 1990 2000 2010Years
Greece
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L L L L L L L L L L L L L L L L R R
R
R L L L L LL
L
L
L
L L L
01
23
4
Lib
era
lizatio
n
1970 1980 1990 2000 2010Years
Hungary
NCNCNCNCNCNCNCNCNCL L L L NCNCNCNCNC
L R
R LL L
L
L
L
R R
RR R
01
23
45
Lib
era
lizatio
n
1970 1980 1990 2000 2010Years
Italy
L L L L L L L L L L L L L L L L L L L L L
L
L
L L L
R R R RR R
0.5
11
.52
2.5
Lib
era
lizatio
n
1970 1980 1990 2000 2010Years
Mexico
L L L R R R R R R R R R R R R R R
R
R
R L
L
L L
L
L L L L R R R1
23
45
Lib
era
lizatio
n
1970 1980 1990 2000 2010Years
Netherlands
L L L L L L L R R R R R
L L L
R
L L
L L
L L L
R
R R R
R R R R L
01
23
4
Lib
era
lizatio
n
1970 1980 1990 2000 2010Years
Norway
L L L L L L L L L L L L L L L L NCNCNCNCNC
L L
L
L
L
L
L
L
L
L R
01
23
45
Lib
era
lizatio
n
1970 1980 1990 2000 2010Years
Poland
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L L NCNCNCNCNCNCR L LR R R R R R R
R R
R
L L L L
L
L
L R R
R L
01
23
4
Lib
era
lizatio
n
1970 1980 1990 2000 2010Years
Portugal
L L NCNCL NCNCNCL L L L L L L L
L R
R
R
L
L L L
L L L L LL L L
12
34
56
Lib
era
lizatio
n
1970 1980 1990 2000 2010Years
Sweden
L NCNCNCL NCNCNCNCR R R R R R
R R R R R R R R
R R
L
L
L NCNCNCNC
01
23
4
Lib
era
lizatio
n
1970 1980 1990 2000 2010Years
Turkey
R R L L L
L R R RR
R R R R R R RR L L
L L L
LL L R R R R R R
22
.53
3.5
44
.5
Lib
era
lizatio
n
1970 1980 1990 2000 2010Years
USA
According to Figure 3, the historical evidence of liberalization policies adopted in seven
network industries (communications, electricity, air transport, gas, post, rail and road)
over the last two decades under left-wing governments – as in Australia, Canada, Czech
Republic, Denmark, France, Greece, Hungary, Italy, Mexico, the Netherlands, Norway,
Poland, Portugal, Sweden and even the USA – shows relevant exceptions to the
empirical findings supporting the view that the right wing favors liberalization and the
left wing is against it.
We point out that the stylized facts shown in the above figures are hard to reconcile
with earlier empirical findings unambiguously attributing a significantly greater impact
of right-wing governments upon liberalization than of left-wing ones, at least with
reference to the most updated OECD sample we have reported and employed in our
analysis (OECD, 2009).
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To contribute to solving the above empirical puzzle, we investigate the following
hypothesis:
Left-wing governments have been as active as right-wing ones in adopting
liberalization of network industries.
In doing so, we also explore whether homogeneous coalitions and majoritarian systems
induce a higher level of liberalization of network industries than do heterogeneous
coalitions and proportional electoral systems.
4. Data and empirical strategy
4.1 Data and variables
In order to perform the empirical analysis we collect a well-suited data set in which we
link information on countries‟ liberalization outcomes to various characteristics of
national governments. We use data from various sources over the 1975-2006 period.
The base sample we use is the largest possible given the data availability (30
countries);1 moreover, our sample period covers entirely the liberalization wave
observed in Western countries in the last three decades through 2006, whereas previous
analyses focused on a smaller number of countries and on a shorter period coverage.
To construct an index of economic liberalization (which we call Liberalization in our
empirical analysis) we use the entry barriers index measured by the Indicators of
Regulation in Energy, Transport and Communications – ETCR – (OECD, 2009) and
1 Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece,
Hungary, Iceland, Ireland, Italy, Japan, Luxembourg, Mexico, Netherlands, New Zealand, Norway,
Poland, Portugal, Slovakia, South Korea, Spain, Sweden, Switzerland, Turkey, United Kingdom, United
States.
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calculate the variable Liberalization by subtracting the OECD (2009) entry barriers
measure from its maximum value. The OECD (2009) entry barriers index is calculated
by OECD as the simple average of seven sectoral indicators that, in turn, measure the
strictness of the legal conditions of entry in the following non-manufacturing sectors:
passenger air transport, telecommunications, electricity, gas, post, rail and road. Our
final Liberalization index ranges from 0 to 6.2
To identify the government party‟s political orientation with respect to economic policy,
we use information from the Database of Political Institutions (World Bank, 2008),
which has been routinely used in cross-country quantitative studies (see, among others,
Dutt and Mitra (2005), Krause and Méndez (2005), and Giuliano and Scalise (2009)).
We construct three dummy variables – Left, Right and Other – which respectively equal
1 if: the government party is defined as socialist, social-democratic, communist or left-
wing (Left); it is defined as conservative, Christian democratic or right-wing (Right); or
it is defined as centrist or does not fit into the two previously mentioned categories
(Other).3
The relation between the executive‟s orientation and economic liberalization outcomes
may be dependent also on a government's other characteristics, the omission of which
might cause estimation bias. For example, as suggested by Bortolotti and Pinotti (2008),
the effective lawmaking power of the government is (possibly) relevant to the
executive‟s capacity to implement economic policies, so that a low legislative power
may affect the executive‟s initiatives regardless of its political orientation. We cope
with this problem by including a set of legislature-specific variables in the econometric
analysis.
In particular, we consider the following variables.
2 For a comprehensive description of the ETCR indicators see Conway and Nicoletti (2006).
3 A detailed description of the main variables that we use is provided in the Appendix.
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GovHeterogeneity: this variable is defined as the probability that two deputies picked at
random from among the government parties will be of different parties (source: World
Bank, 2008);
Majority: this measures the margin of majority, that is the fraction of seats held by the
government, calculated by dividing the number of government seats by total seats
(source: World Bank, 2008);
AllHouse: this is a dummy variable that equals 1 when the party of the executive has an
absolute majority in the houses that have lawmaking powers (source: World Bank,
2008);
YearsInOffice: this is defined as the number of years the chief executive has been in
office (source: World Bank, 2008);
YearsLeft: this is the number of years left in the current term (source: World Bank,
2008);
Proportional: this is a dummy variable equal to 1 if representatives are elected based on
the percentage of votes received by their party and/or the electoral system is specifically
called „proportional representation‟ (source: World Bank, 2008).
Several authors highlight that a government‟s economic policies do not depend only on
the executive‟s political motivation and lawmaking power but are shaped also by the
country‟s economic characteristics. We control for this possibility and consider a set of
further covariates.
First, we use government debt as a percentage of GDP (GovDebt) in order to control for
the central government's financial situation. This variable measures the entire stock of
direct government fixed-term contractual obligations to others, outstanding on a
particular date. It includes domestic and foreign liabilities such as currency and money
deposits, securities other than shares, and loans; it is the gross amount of government
liabilities reduced by the amount of equity and financial derivatives held by the
government (source: World Development Indicators, World Bank, 2009).
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Second, the country‟s general economic situation as measured by the gross domestic
product may be important as well. Thus we include GDP converted to 2005
international dollars using purchasing power parity rates (Gdp), and its per capita value
(GdpPerCap). Both these variables are obtained from World Bank (2009).
Third, labor market conditions are another factor potentially relevant to economic
liberalization. For instance, Blanchard and Giavazzi (2003) point out that product and
labor market regulation are likely to be linked. Accordingly, we consider an indicator of
the degree of employment protection (EmplProtection), obtained from OECD (2008),
the employment in industry as a percentage of total employment (Employment),
obtained from World Bank (2009). EmplProtection is calculated as an unweighted
average of 12 sub-indicators for regular contracts and six sub-indicators for temporary
contracts; this variable is a synthetic index of the strictness of the country‟s employment
protection legislation.
Fourth, we include a dummy variable that records whether a given country has a civil
law legal system – CivilLaw – (source: La Porta et al., 1998). Pitlik (2007) finds
evidence that „legal families‟ play a role in affecting market-oriented policies.
Finally, we also include two dummy variables that record, respectively, the country‟s
membership of the European Union (EUMember) and adoption of the euro (Euro) to
test whether policy diffusion plays a role in affecting parties in office, irrespective of
their political ideology, as a supranational driver of national governments‟ initiatives
(Levi-Faur, 2003; Simmons and Elkins, 2004; Comin and Diaz Fuentes, 2006; Høj,
Galasso, Nicoletti and Dang, 2006; Pitlik, 2007).
TABLE 1. Descriptive statistics (left-wing and right-wing governments).
Right Left
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Variable Obs. Mean Obs. Mean Source
Liberalization
359 1.889 336 2.042 Authors‟ elaboration on OECD
(2009) data World Bank (2008)
Majority 294 0.614 301 0.668 World Bank (2008)
AllHouse 358 0.276 331 0.280 World Bank (2008)
YearsInOffice 359 3.988 335 3.814 World Bank (2008)
YearsLeft 359 1.682 334 1.652 World Bank (2008)
GovHeterogeneity 354 0.288 326 0.194 World Bank (2008)
GovDebt 101 56.638 113 59.564 World Bank (2008)
EmplProtection 246 2.166 236 2.233 OECD (2008)
Employment 287 28.608 247 28.308 World Bank (2009)
Gdp 320 1.22e+12 284 8.78e+11 World Bank (2009)
GdpPerCap 320 23281.64 284 23242.69 World Bank (2009)
EUMember 359 0.200 336 0.300 Authors‟ coding
Euro 359 0.075 336 0.026 Authors‟ coding
Proportional 358 0.793 332 0.789 World Bank (2008)
CivilLaw 359 0.239 336 0.214 La Porta et al. (1998)
Note: we have dropped from the sample the Czech Republic‟s and Hungary‟s observations
referring to the years of communist dictatorship, while Slovakia‟s observations refer to the
period after it was declared a sovereign state; Switzerland is removed from the final sample
because of missing data on the main political characteristics of the government.
Generally, as policy initiatives take time to generate an observable economic (or legal)
outcome, we regress our liberalization variable on one-year-lagged covariates. So, on
the one hand, we avoid attributing an economic outcome, resulting perhaps from a
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laborious political process, to an executive just elected; on the other hand, we do not
incur endogeneity or reverse causality problems due to the simultaneous determination
of liberalization and certain types of labor market institutions (as measured by
EmplProtection) and/or the general economic conditions of countries (measured, for
instance, by Gdp).
In our final dataset, the countries with the highest liberalization score at the end of the
period considered are Denmark (5.7), Germany (5.6) and Sweden (5.5), while those
with the lowest are Mexico (2.4), South Korea (3.1) and Turkey (3.6). The countries
that saw left-oriented governments most often between 1975 and 2006 are Austria,
Canada, Mexico and Sweden; conversely, Belgium, Japan and South Korea show the
highest number of right-oriented governments in the period under consideration. On
average, the government heterogeneity index is about 0.25 and the margin of majority
of governments is about 0.64 (both these indexes range from 0 to 1).
A synthetic description of all the variables is provided in Table 1.
4.2 Empirical strategy
Even if we include in our analysis all the variables which we deem potentially relevant
to economic liberalization, it may be argued that unobservable or unmeasurable factors,
such as national culture or traditions, also affect the economic policies of governments
(La Porta et al., 1999; Guiso, Sapienza and Zingales, 2006). Furthermore, time
tendencies may shape countries‟ liberalization patterns regardless of the political beliefs
of individual governments (e.g., the „globalization wave‟).
To cater for this, we undertake a panel analysis in which we include country- and time-
fixed effects. Time-fixed effects, in particular, capture the time pattern that is constant
across countries. Having done so, what we finally obtain is an estimate of the marginal
effect of country-specific and legislature-specific variables on the variations of
liberalization outcomes across countries and years, also controlling for time tendencies.
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Formally, we focus on the following population regression function:
scsctionLiberalizat tt βxxE 0,, (1)
where x = 1xt1 +…+ KxtK, and xtj indicates variable j at time t, and where the omitted
variable country-specific c is time-constant, while the omitted variable time-specific s is
constant across countries. In model (1) it is assumed that c has the same effect on the
mean response in each time period and that s has the same effect on the mean response
in each country.
In our context, the basic unobserved effects model (UEM) can be written as:
Liberalization it= βxit+ci+st +uit , t = 1975, 1976, …, 2006 (2)
where xit is a 1 K vector, ci and st are unobserved components which account for the
unobserved heterogeneity, and uit are idiosyncratic disturbances that change across t and
across i.
In a fixed-effects estimation, ci and st are allowed to be correlated with xit, so that the
assumption of zero correlation between the observed explanatory variables and the
unobserved effects is not imposed. By doing so, we obtain results that are more robust
than those obtained through a random effects analysis. However, as Wooldridge (2002)
points out, this robustness comes at a price; specifically, we cannot include factors
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constant across countries and periods in xit. In our estimation, we respect this
restriction.4
Finally, we calculate one-period-lagged values of the explanatory variables and include
them in the operative model. Hence, making the response variable and the government‟s
political orientation explicit, we can write model (2) in the following lag UEM form:
ittiititit uscRight-wingβLeft-wingβtionLiberaliza -1it-1itλω δκ 12110 (3)
with t = 1975, 1976, …, 2006, and where κit-1 is a vector that contains the legislature-
specific variables, ωit-1 is a vector of controls referring to the country‟s economic
characteristics, δ and λ are vectors of parameters, 0 is the model constant, ci and st are
unobserved components which account respectively for the unobserved country-specific
(constant over time) and time-specific (constant across countries) heterogeneity, and uit
are idiosyncratic disturbances that change across t and i.
Notice that Otherit-1 is the benchmark class for the government‟s political orientation
dummies.
As a robustness check, we perform two model specifications in which we consider also
the one-year lagged deregulation level (observed in each country) and a linear time
trend explicitly included as one of the covariates.
When we include the one-year-lagged deregulation level, model (3) becomes:
4 Note that in one model specification, we include a country‟s legal family variable (CivilLaw) that is time
constant. In order to perform this individual regression, we use a random effects model.
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1312110 itititit onDeregulatiβRight-wingβLeft-wingβtionLiberaliza
itti usc -1it-1itλω δκ (3′)
The deregulation level is measured using an indicator obtained by subtracting the
OECD (2009) ETCR index to its maximum value. The OECD (2009) ETCR index is
calculated by OECD as the simple average of seven sectoral indicators that, in turn,
measure the strictness of the legal conditions of entry, the level of public ownership, the
characteristics of vertical integration and the market structure in the above mentioned
non-manufacturing sectors. Including the one-year-lagged deregulation level allows us
to estimate the autoregressive component of liberalization policies. By doing so, we can
observe the effect of governments’ political orientation independently of the existing
regulatory conditions which executives find when elected.
When we include the time trend, model (3) can be written as:
itititit TrendβRight-wingβLeft-wingβtionLiberaliza312110
itti usc -1it-1itλω δκ (3′′)
Including a time trend allows us to estimate the effect, if any, of time factors
(independent of country-specific variables) influencing the average pattern of
deregulation in OECD countries. As will be shown in the next section, estimation
results remain substantially similar across different model specifications.
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5. Results
The estimation results are presented in Table 2. We have considered 15 panel model
specifications, which are constructed in such a way that multicollinearity problems are
avoided. Notice that the model specifications from (1) to (15) show an increasing
explicative power, as we progressively add control variables. In the last specification –
(15) – the R-square indicates that the proportion of variability in liberalization outcomes
that is accounted for by the statistical model is almost 80%.
In all the model specifications in which the governments‟ political orientation dummies
are included, we find that left-oriented governments have a positive and statistically
significant effect on the observed level of economic liberalization. Conversely, right-
oriented governments do not show statistically significant effects in most of the model
specifications, while they show a positive and statistically significant effect in models
(11), (13), (14) and (15). Notice, moreover, that the estimated effect of right-wing
governments on liberalization always shows a lower intensity and a lower statistical
significance than those of left-wing governments.
It is worth emphasizing that we estimate the effect of left-wing and right-wing political
ideology with respect to centrist or non-classifiable governments (as they are defined in
the Appendix); thus, what we are able to infer is the effect of a given political
orientation relative to another and not the absolute effect. It follows that our result must
be interpreted as a sign of an influence of left-oriented governments on liberalization
which is greater than that of right-oriented governments, while our estimation result
does not indicate that right-wing executives do not liberalize in absolute terms.
With respect to the institutional determinants of liberalization in network industries, we
obtain several interesting results. First, we find that the government‟s heterogeneity
(GovHeterogeneity), which is a proxy of the political fragmentation of the government,
has a positive influence on liberalization. Second, model specifications (3), (7), (8), (10)
and (11) show that the margin of majority of the government‟s parties (Majority) has a
negative effect on the level of economic liberalization, which is consistent with the
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previous result. Third, in model specifications (7) and (8) we also find a positive effect
of proportional representation systems (Proportional).5 The variables AllHouse,
YearsInOffice and YearsLeft, on the other hand, do not turn out to be statistically
significant.
When we move to consider pre-existing country economic conditions, we obtain further
interesting results. Model specifications (12), (13), (14) and (15), indeed, show that the
strictness of the employment protection legislation (EmplProtection) is a negative and
statistically significant influence on economic liberalization, and so too is the relative
amount of employment in industry (Employment), as shown in model (15).
Supranational drivers of liberalization initiatives may be important as well. In particular,
model specifications (13), (14) and (15) show that EU membership (EUMember) plays
a positive and statistically significant role in the reduction of entry barriers. A positive
and statistically significant effect is also found for the introduction of the euro (as has
already been suggested by Høj, Galasso, Nicoletti, and Dang (2006)), as is shown by
model specification (12). Estimated coefficients suggest, in particular, that the effect of
EU membership is greater than that of adoption of the euro.
Finally, models (12) and (15) reveal that the country‟s GDP (Gdp) has a negative effect,
while models (12), (13) and (15) show that the GDP per capita (GdpPerCap) has a
positive one. According to model specification (11), the legal family (CivilLaw) seems
to be statistically irrelevant.
As a robustness check, in model specifications (14) and (15), we have added to country-
and time-fixed effects respectively the one-year-lagged value of deregulation and a
linear time trend, in order to estimate the (possibly) relevant effect of time patterns that
5 Unreported estimations show that in the sub-group of countries that adopt proportional representation
systems, the effect of the government‟s colour has a lower intensity than elsewhere. It is also worth
noting that the representation system that countries adopt is not correlated in a statistically significant way
with the probability of having a left-wing or a right-wing government.
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are virtually constant across countries. While the estimated parameters relating to both
these additional factors turn out to be positive and statistically significant, our main
results do not change and are shown to be robust across the different specifications.
Specifically, the positive effect exerted by the one-year-lagged value of deregulation
indicates that liberalization policies follow a path in which past initiatives stimulate
subsequent interventions, in a progressive process characterized by a ratchet effect. This
confirms a strong path-dependency effect of liberalization policy in network industries.
The diagnostic analysis, furthermore, allows us to reject the null hypothesis of joint
statistical insignificance of all the parameters, in all the considered model specifications.
Consistently, our findings show that partisanship and political institutions have a
significant, predictable impact on the intensity of liberalization policy in network
industries, with left-wing parties choosing higher levels of liberalization compared to
right-wing parties.6
Our main findings contrast with some statistical results in extant empirical literature
(e.g., Pitlik, 2007; Potrafke, 2009). While we find that left-wing governments have a
positive impact on liberalization in network industries and that the effect of right-wing
governments is lower in terms of both estimated intensity and statistical significance,
previous investigations, considering a shorter period and a smaller sample, show the
opposite7.
6 Notice that unreported GLM estimations, in which we add alternatively time and country dummies,
show substantially similar final results. These estimations are available upon request. show substantially
similar final results. These estimations are available upon request.
7 We believe that such a difference is mainly due to two elements.On the one hand, we use different
variables in order to account for the effect of governments‟ political ideology. For instance, the two
indices used by Potrafke (2010) are compact variables which oppose left with right and which weight the
ideology scores with the government party‟s relative share of seats in parliament; similarly, Pitlik (2007)
measures political orientation of governments by means of a five-year averaged index of left-wing party
cabinet positions over total cabinet seats. The World Bank (2008) index that we use, in contrast, is
composed of a set of three dummies that allow us to study separately the effect of right-wing and left-
wing governments with respect to that of non-classifiable governments, which are considered as the
reference group. Moreover, we do not weight the index for the relative lawmaking power of the
executive‟s leading party, while we include various measures of lawmaking power of governments as
separate covariates. On the other hand, we consider a longer period than that examined by previous
literature along with a larger set of factors. Our estimation results partially converge with those of Pitlik
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(2007) and Potrafke (2010) when we restrict our analysis to the 1975-1999 period, as unreported
estimations show.
27
TABLE 2. Cross-country panel estimation results. Dependent variable: Liberalization.
(1)
PANEL
(2)
PANEL
(3)
PANEL
(4)
PANEL
(5)
PANEL
(6)
PANEL
(7)
PANEL
Variable Coef. (Std.Err.) Coef. (Std.Err.) Coef. (Std.Err.) Coef. (Std.Err.) Coef. (Std.Err.) Coef. (Std.Err.) Coef. (Std.Err.)
Left 0.430 (0.197) ** 0.473 (0.204) ** 0.545 (0.225) ** 0.432 (0.200) ** 0.415 (0.198) **
Right 0.240 (0.193) 0.161 (0.198) 0.342 (0.221) 0.242 (0.197) 0.212 (0.195)
GovHeterogeneity 0.903 (0.354) ** 0.689 (0.344) **
Majority -2.419 (0.381) *** -2.513 (0.383) ***
AllHouse -0.292 (0.187)
YearsInOffice 0.025 (0.020)
Proportional 2.913 (0.539) ***
YearsLeft
28
GovDebt
Employment
EmplProtection
Gdp
GdpPerCap
Euro
EUMember
CivilLaw
Deregulation
Trend
Constant 1.648 (0.163) *** 1.466 (0.195) *** 3.240 (0.305) *** 1.729 (0.169) *** 1.565 (0.176) *** 1.785 (0.103) *** 1.352 (0.503) ***
Time and country FE Yes Yes Yes Yes Yes Yes Yes
Time and country RE No No No No No No No
Number of obs. 805 782 681 795 802 782 666
29
F-Statistics 7.02 6.68 7.54 6.22 6.77 7.05 8.76
H0: bi=bj=0 (p-v.) 0.000 0.000 0.000 0.000 0.000 0.000 0.000
R-sq. 0.014 0.016 0.078 0.027 0.021 0.002 0.001
Note: * = 0.10 confidence level, ** = 0.05 confidence level, *** = 0.01 confidence level.
TABLE 2. (Continued)
(8)
PANEL
(9)
PANEL
(10)
PANEL
(11)
PANEL
(12)
PANEL
(13)
PANEL
(14)
PANEL
(15)
PANEL
Variable Coef. (Std.Err.) Coef. (Std.Err.) Coef. (Std.Err.) Coef. (Std.Err.) Coef. (Std.Err.) Coef. (Std.Err.) Coef. (Std.Err.) Coef. (Std.Err.)
Left 0.488 (0.233) ** 0.464 (0.206) ** 0.515 (0.234) ** 0.574 (0.224) *** 0.432 (0.161) *** 0.586 (0.273) * 0.446 (0.215) ** 0.224 (0.096) **
Right 0.251 (0.230) 0.167 (0.202) 0.313 (0.230) 0.368 (0.221) * 0.226 (0.159) 0.526 (0.277) * 0.408 (0.218) * 0.180 (0.095) *
GovHeterogeneity 0.845 (0.368) ** -0.100 (0.141)
Majority -2.597 (0.384) *** -2.349 (0.391) *** -2.311 (0.379) ***
AllHouse -0.160 (0.199) -0.214 (0.196) -0.239 (0.185) 0.042 (0.073)
30
YearsInOffice 0.020 (0.021) 0.038 (0.024) 0.042 (0.023) * -0.008 (0.008)
Proportional 2.888 (0.537) ***
YearsLeft -0.010 (0.043) 0.013 (0.046) 0.010 (0.045) -0.019 (0.016)
GovDebt 0.007 (0.006) 0.007 (0.004)
Employment 0.058 (0.035) -0.057 (0.014) ***
EmplProtection -1.259 (0.119) *** -1.056 (0.186) *** -0.757 (0.148) *** -0.326 (0.078) ***
Gdp -0.001 (0.000) ** 0.001 (0.001) -0.001 (0.000) ** -0.001 (0.001)
GdpPerCap 0.001 (0.000) *** 0.001 (0.000) *** -0.000 (0.000) 0.001 (0.000) ***
Euro 0.422 (0.149) ***
EUMember 1.584 (0.237) *** 1.165 (0.183) *** 0.347 (0.074) ***
CivilLaw -0.064 (0.428)
Deregulation 0.967 (0.041) ***
Trend 0.259 (0.026) ***
Constant 1.109 (0.535) *** 1.461 (0.239) *** 3.118 (0.348) *** 3.190 (0.385) *** -1.278 (0.444) *** -4.562 (1.005) *** -3.852 (1.275) *** 1.834 (0.514) ***
31
Time and country FE Yes Yes Yes No Yes Yes Yes Yes
Time and country RE No No No Yes No No No No
Number of obs. 782 681 795 672 538 204 193 465
F-Statistics 8.42 6.14 6.57 -- 31.87 18.93 12.54 19.73
H0: bi=bj=0 (p-v.) 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
R-sq. 0.001 0.023 0.088 0.091 0.416 0.415 0.241 0.793
Note: * = 0.10 confidence level, ** = 0.05 confidence level, *** = 0.01 confidence level
© Filippo Belloc & Antonio Nicita
6. Discussion
Our analysis confirms that politics matter for liberalization of network industries
(Duso, 2002; Høj, Galasso, Nicoletti, and Dang, 2006; Pitlik, 2007; Potrafke, 2010).
The conclusion we reach - that right-wing governments on average liberalize less than
left-wing ones - while reversing related literature, is rather general, being found in all
the specifications analyzed and taking into account the existing regulatory conditions
that executives are faced with when elected. This means that our results are fairly
robust and are not affected by initial local conditions, in one sense or another.
Figure 4 outlines the estimated liberalization trends of left-wing governments for each
of the model specifications reported in Table 2, in which political orientation
dummies are included. Trends are calculated considering the estimated effect of
having a left-wing government rather than another one, starting from an initial
situation of null liberalization, and considering this effect, for each time interval, with
respect to the previous level of liberalization. As we can see, all the model
specifications confirm a significant growing trend in left-wing liberalizations. The one
referring to the last specification (15), which includes the largest set of control
variables, shows a similar but flatter trend. Figure 4 also outlines the strong path-
dependency effect we actually found for every political color in office. The level of
past deregulation turns out to be particularly relevant, suggesting that liberalization
processes follow a progressive path with a ratchet effect, both for the aggregate
indicators and for sectoral ones.
Moreover, as the estimated positive effect of the autoregressive component suggests,
rather than observing strong discontinuity and shocks, we registered a gradual
implementation of liberalization policies. This implies that once a liberalization
process is launched, its intensity may depend on the political color of the party in
office, but it is never retracted.
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Figure 4
Estimated liberalization trends of left-wing governments (coefficients derived from
Table 2).
Model (15)
Models (1), (2), (3),
(4), (5), (8), (9), (10),
(11), (12), (13), (14)
0
1
2
3
4
5
6
1975 1980 1985 1990 1995 2000 2005
Surprisingly, we did not find a statistically significant effect of legal origins on
network industry liberalization, contrary to what might be expected according to the
general insights of La Porta et al. (1999) and Pitlik‟s (2007) findings for liberalization
policies. We cannot however exclude that this could be due to the strong role played
by the country-specific variables that we considered in our analysis, which are
partially correlated with the legal origin of nations. Some legal scholars, in addition,
have recently cast doubts on the assumption that legal origins are the foundation of
legal institutions and economic outcomes (see, for example, Roe, 2006).
As to political determinants, our findings contrast with the empirical literature
available so far (Pitlik, 2007; Potrafke, 2010), probably due, as we argued, to the
broader dimensions of the sample we study - both in terms of OECD countries
included and number of years covered – and to the different indicator we adopted for
governments‟ political color, with respect to previous analyses.
The „political economy of economic policies‟ so far has simply neglected the left-
wing issue, falling into line with conventional wisdom, confirmed by the empirical
literature, on significantly greater right-wing adoption of other market-oriented
policies, such as privatization. However, the liberalization paths we observed in
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network industries force economic theory to give a reason for the political-economic
rationale behind them.
We do not attempt here to solve this puzzle, rather we simply raise insights and
questions for further research on the issue.
7. Conclusions
According to conventional wisdom, right-wing parties should, in principle, promote
market-oriented outcomes, as this is embedded in their traditional ideological
adherences. This prediction has been confirmed by many empirical findings with
reference to privatization policies. Some recent empirical findings seem to confirm
these results also for liberalization policies in network industries, arguing that the
likelihood of liberalization increases under majoritarian rules and right-wing
governments.
Firstly, looking at some stylized facts we have argued that these results contrast with
single-nation case studies, such as Austria, Canada, France, Italy, Mexico and Sweden
among others, where left-wing governments have significantly introduced
liberalization in network industries.
Then we have investigated, through an econometric analysis, whether right-wing
governments have been more active in adopting liberalization of network industries
than left-wing ones, and whether homogeneous coalitions and majoritarian systems
have induced a higher level of liberalization of network industries than heterogeneous
coalitions and proportional electoral systems have done. Our sample included the
largest and most updated data set available on 30 OECD countries, provided by the
latest releases of ETCR economic indicators for the liberalization of seven network
industries (OECD, 2009) and by political indicators of the World Bank‟s Database of
Political Institutions (World Bank, 2009). This allows us to study a larger group of
countries with respect to previous empirical studies and to perform the first
econometric analysis that considers the entire liberalization wave observed in OECD
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countries from the Seventies to date. By so doing, we show that a study of the
liberalization wave in its entirety reverses the findings reached so far by empirical
literature on liberalization of network industries.
Contrary to traditional ideological cleavages, we find that right-wing governments
liberalize less than left-wing ones. This result is confirmed when controlling for the
existing regulatory conditions that executives find once elected.
In particular, in all the model specifications in which we have included the
executive‟s political orientation dummies, we have found that left-oriented
governments had a positive and statistically significant effect on the observed level of
economic liberalization, while right-oriented governments did not show statistically
significant effects or showed a smaller one. We have found also that EU membership
and adoption of the euro strongly encouraged liberalization policies.
We believe our findings are relevant in two main respects.
First, they question the conventional wisdom predicting that all market-oriented
policies – including liberalization and privatization in network industries – are almost
exclusively adopted by right-wing governments, as the consequence of traditional
ideological cleavages for governments‟ policy choices. We show not only that left-
wing governments do liberalize network industries, but that they progressively even
increased the intensity of liberalization over time, in almost all the seven sectors
studied. On the contrary, right-wing liberalization registered a smooth, but significant,
decline. This conclusion implies that, for some reasons to be further investigated,
liberalization of network industries differs from other market-oriented policies in
terms of its political appeal and rationale. Thus, one first consequence of our
investigation is that the measurement of political determinants of market-oriented
policies in network industries should disentangle liberalization and other deregulation
policies.
Secondly, our results raise a new puzzle with more general implications for the
theoretical analysis of the political-economic meaning and extent of liberalization
policy in network industries: why do left-wing parties in office liberalize, and why do
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they seem to liberalize, on average, to a greater extent than right-wing governments
do?
Our results contrast with the superficially self-evident thesis that right-wing
governments should always maintain the same aligned incentives towards
privatization and liberalization. Left-wing governments have been at least as active as
right-wing ones in adopting liberalization of network industries. Our empirical results
confirm that, contrary to conventional wisdom, the political-economic rationale
behind liberalization paths in network industries is far from being assessed and a
comprehensive theoretical framework on the political determinants of liberalization
policies is still needed.
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Appendix: main indicators’ description.
Variable Description
Liberalizati
on
We calculate the variable Liberalization by subtracting the
OECD‟s (2009) entry barriers measure from its maximum value.
The OECD‟s (2009) entry barriers index is calculated by OECD as
the simple average of seven sectoral indicators that, in turn,
measure the strictness of the legal conditions of entry in the seven
non-manufacturing sectors. The OECD‟s (2009) sectoral indicators
focus on sector-specific aspects of entry regulation, as follows.
Passenger air transport: the focus is on open skies agreements
with the USA, regional agreements, and restriction on the number
of domestic airlines allowed to operate on domestic routes.
Telecom: the focus is on the legal conditions of entry into the
trunk telephony market, the international market, and the mobile
market. Electricity: the focus is on the conditions of third-party
access to the electricity transmission grid and on the conditions of
the competition in the market for electricity. Gas: the focus is on
the conditions of third-party access to the gas transmission grid, on
the share of the retail market open to consumer choice, and on the
existence of any regulation that restricts the number of competitors
allowed to operate in the market. Post: the focus is on the existence
of any regulation that restricts the number of competitors allowed
to operate in the national market of basic letter services, basic
parcel services, and courier activities. Rail: the focus is on the
legal conditions of entry into the passenger and the freight
transport rail markets. Road: the focus is on the criteria considered
in decisions on entry of new operators.
Left
Dummy variable that equals 1 for parties that are defined as social
democratic, left-wing, communist, socialist (source: World Bank,
2008).
Right
Dummy variable that equals 1 for parties that are defined as
conservative, Christian democratic, or right-wing (source: World
Bank, 2008).
Other
Dummy variable that equals 1 for parties that are defined as
centrist, and for all those cases which do not fit into the above-
mentioned categories (e.g. party‟s platform does not focus on
economic issues), or no information (source: World Bank, 2008).